Evolution of the semiconductor manufacturing industry is placing ever greater demands on yield management and, in particular, on metrology and inspection systems. Critical dimensions are shrinking while wafer size is increasing. Economics is driving the industry to decrease the time for achieving high-yield, high-value production. Thus, minimizing the total time from detecting a yield problem to fixing it determines the return-on-investment for the semiconductor manufacturer.
Fabricating semiconductor devices, such as logic and memory devices, typically includes processing a semiconductor wafer using a large number of fabrication processes to form various features and multiple levels of the semiconductor devices. For example, lithography is a semiconductor fabrication process that involves transferring a pattern from a reticle to a photoresist arranged on a semiconductor wafer. Additional examples of semiconductor fabrication processes include, but are not limited to, chemical-mechanical polishing (CMP), etch, deposition, and ion implantation. Multiple semiconductor devices may be fabricated in an arrangement on a single semiconductor wafer and then separated into individual semiconductor devices.
Extreme ultraviolet (EUV) lithography increases the need for robust detection of repeater defects. There are no actinic light mask inspectors for EUV. Thus, the task of mask inspection shifts from mask-inspectors to wafer-inspectors. The frequency of in-line mask inspections can be rather high since there is no pellicle for EUV masks and the masks are exposed during operation.
Defect review for advanced design rules can be for objects that are quite small (e.g., for detection of defects below 10 nm), so hot scans may be run to catch such defects. A “hot scan” generally refers to a measurement/inspection of a wafer performed to detect defects or take measurements on the wafer by applying relatively aggressive detection settings (e.g., thresholds substantially close to the noise floor). In this manner, the hot scan may be performed to collect inspection or measurement data about the wafer that will be used for the tuning process (optics selection and algorithm tuning). The goal of the hot scan may be to detect a representative sample of all defect and nuisance types on the wafer in the selected mode(s).
Repeater detection (e.g., with coordinates matching) can be a strong filter that can bring the nuisance density to manageable levels. However, a repeater defect detection (RDD) algorithm is typically performed as the last inspection step and requires all defects to be collected in the final lot result. Hot inspections required for mask qualification may result in millions of defect candidates prior to RDD. This can cause tool choking and dropped defects when transferring results from the inspection system to the high level defect detection controller. RDD may be subject of the same limitations of maximum number of defects and defects density as random defect detection inspection, although the final number of defects of interest (DOIs), such as repeaters, may be reasonably small. It should be noted that many repeater defects are “soft” repeaters. Soft repeaters are not printed in every reticle due to process variation. This means that it may not be possible to use in-job RDD while being able to analyze results for the whole wafer.
With feature shrink and a potential resolution limit for optical wafer inspection tools, the primary candidate inspection tool for print check is an electron beam inspection tool, such as a scanning electron microscope (SEM). However, electron beam inspection tools have a throughput disadvantage. With the best scenario of multiple beam/column options, the estimated inspection time for one reticle is more than 8 hours. Broad band plasma (BBP) tools have much higher throughput and, hence, coverage. In the current BBP tool design, repeater analysis is part of the post-processing step in the high level defect detection controller. Due to architecture limitations (both software and hardware), there may be a limit of less than 10 million defects that result using current BBP tool configurations. However, estimations show that about 10 billion defects may need to be handled. Optimization of throughput and development time is a serious challenge for RDD.
Furthermore, current inspections are performed using die-to-die comparisons and show high levels of nuisance due to design systematic nuisance events. These nuisance events can be real defects, but are known to be non-critical defects. Detection of DOI can be impaired because there are too many systematic nuisance events.
Therefore, improved RDD is needed.